
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE						       =						{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE						       =						["""XLMRobertaTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE						       =						["""XLMRobertaTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE						       =						[
							    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XLMRobertaForCausalLM""",
							    """XLMRobertaForMaskedLM""",
							    """XLMRobertaForMultipleChoice""",
							    """XLMRobertaForQuestionAnswering""",
							    """XLMRobertaForSequenceClassification""",
							    """XLMRobertaForTokenClassification""",
							    """XLMRobertaModel""",
							    """XLMRobertaPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE						       =						[
							    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXLMRobertaForCausalLM""",
							    """TFXLMRobertaForMaskedLM""",
							    """TFXLMRobertaForMultipleChoice""",
							    """TFXLMRobertaForQuestionAnswering""",
							    """TFXLMRobertaForSequenceClassification""",
							    """TFXLMRobertaForTokenClassification""",
							    """TFXLMRobertaModel""",
							    """TFXLMRobertaPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE						       =						[
							    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxXLMRobertaForMaskedLM""",
							    """FlaxXLMRobertaForCausalLM""",
							    """FlaxXLMRobertaForMultipleChoice""",
							    """FlaxXLMRobertaForQuestionAnswering""",
							    """FlaxXLMRobertaForSequenceClassification""",
							    """FlaxXLMRobertaForTokenClassification""",
							    """FlaxXLMRobertaModel""",
							    """FlaxXLMRobertaPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

							__SCREAMING_SNAKE_CASE						       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Optional[int]						=    len(lowerCamelCase    )
				# We need to create solution object to save path.
				UpperCamelCase_:				List[str]						=    [[0 for _ in range(lowerCamelCase    )] for _ in range(lowerCamelCase    )]
				UpperCamelCase_:				Dict						=    run_maze(lowerCamelCase  ,     0  ,     0  ,     lowerCamelCase    )
				if solved:
								print("""\n""".join(str(lowerCamelCase    ) for row in solutions    )    )
				else:
								print("""No solution exists!"""    )
				return solved







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Dict						=    len(lowerCamelCase    )
				# Final check point.
				if i == j == (size - 1):
								UpperCamelCase_:				Union[str, Any]						=    1
								return True

				UpperCamelCase_:				List[str]						=    (not i < 0) and (not j < 0)  # Check lower bounds
				UpperCamelCase_:				List[str]						=    (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								UpperCamelCase_:				Dict						=    (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												UpperCamelCase_:				Optional[Any]						=    1

												# check for directions
												if (
												    run_maze(lowerCamelCase  ,     i + 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j + 1  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     i - 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j - 1  ,     lowerCamelCase    )
												):
																return True

												UpperCamelCase_:				Optional[Any]						=    0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()






